Abstract
Introduction
Although observational studies have established associations between three major liver enzymes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT)) and type 2 diabetes (T2D), their ability to improve T2D identification using machine learning (ML) approaches remains underexplored. This study aimed to develop and validate ML-based diagnostic models for identifying T2D by incorporating these liver enzymes.
Methods
Data from two independent cohorts were analyzed: the US National Health and Nutrition Examination Survey (N = 15,528) and a Chinese health examination database (N = 4,952). Twelve demographic and biochemical features were combined with liver enzymes to train eight ML models, including logistic regression, support vector machine, random forest, K-nearest neighbors, classification and regression trees, gradient boosting decision tree, LightGBM, and XGBoost. Model performance was assessed using standard metrics including accuracy, precision, recall, F1-score, and area under the curve (AUC).
Results
Incorporating liver enzymes consistently improved model performance across the algorithms. The XGBoost model showed particularly strong performance, with baseline metrics (accuracy = 0.696, precision = 0.314, F1 = 0.450, AUC = 0.803) increasing to 0.743, 0.346, 0.468, and 0.814, respectively, after inclusion of liver enzymes. Comparable improvements were observed across algorithms and in both the US and Chinese cohorts.
Conclusions
ML models incorporating routinely measured liver enzymes improve T2D identification across US and Chinese datasets. These findings indicate the potential utility of liver enzymes as accessible adjunctive indicators for diabetes risk stratification and improved T2D identification.